Deviation distance entropy: A method for quantifying the dynamic features of biomedical time series

Liu, Weimin; Yang, Bing; Li, Xiaorong; Chen, Jie; Fu, Guohua

doi:10.1016/j.chaos.2023.113157

Cited by 9 publications

(2 citation statements)

References 34 publications

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“…The attention mechanism in human vision mainly helps humans to selectively focus on the part of the information and assign the points that people pay attention to 31–33 . Zheng et al 34 introduced the Multi‐TransSP model, which incorporates both nasopharyngeal carcinoma CT image data and tabular data, to achieve noteworthy outcomes.…”

Section: Related Workmentioning

confidence: 99%

“…The attention mechanism in human vision mainly helps humans to selectively focus on the part of the information and assign the points that people pay attention to. [31][32][33] Zheng et al 34 introduced the Multi-TransSP model, which incorporates both nasopharyngeal carcinoma CT image data and tabular data, to achieve noteworthy outcomes. Wen et al 35 proposed the TransMTDP network for predicting dose distribution in cancer radiotherapy, where the Transformer captures the long-term dependence of multiple dose maps.…”

Section: Combining Attention Mechanism For Alzheimer's Recognitionmentioning

confidence: 99%

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MMTFN: Multi‐modal multi‐scale transformer fusion network for Alzheimer's disease diagnosis

Miao,

Xu,

et al. 2023

Int J Imaging Syst Tech

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Alzheimer's disease (AD) is a severe neurodegenerative disease that can cause dementia symptoms. Currently, most research methods for diagnosing AD rely on fusing neuroimaging data of different modalities to exploit their heterogeneity and complementarity. However, effectively using such multi‐modal information to construct fusion methods remains a challenging problem. To address this issue, we propose a multi‐modal multi‐scale transformer fusion network (MMTFN) for computer‐aided diagnosis of AD. Our network comprises 3D multi‐scale residual block (3DMRB) layers and the Transformer network that jointly learns potential representations of multi‐modal data. The 3DMRB with multi‐scale aggregation efficiently extracts local abnormal information related to AD in the brain. We conducted five experiments to validate our model using MRI and PET images of 720 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The experimental results show that our proposed network outperformed existing models, achieving a final classification accuracy of 94.61% for AD and Normal Control.

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Section: Related Workmentioning

confidence: 99%

Section: Combining Attention Mechanism For Alzheimer's Recognitionmentioning

confidence: 99%

MMTFN: Multi‐modal multi‐scale transformer fusion network for Alzheimer's disease diagnosis

Miao,

Xu,

et al. 2023

Int J Imaging Syst Tech

Self Cite

View full text Add to dashboard Cite

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Enabling inductive knowledge graph completion via structure-aware attention network

Wang

Liu

et al. 2023

Appl Intell

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Decoupling representation contrastive learning for carbon emission prediction and analysis based on time series

Liu,

Hu,

et al. 2024

Applied Energy

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Deviation distance entropy: A method for quantifying the dynamic features of biomedical time series

Cited by 9 publications

References 34 publications

MMTFN: Multi‐modal multi‐scale transformer fusion network for Alzheimer's disease diagnosis

MMTFN: Multi‐modal multi‐scale transformer fusion network for Alzheimer's disease diagnosis

Enabling inductive knowledge graph completion via structure-aware attention network

Decoupling representation contrastive learning for carbon emission prediction and analysis based on time series

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